Flexible nonlinear modeling reveals age-related differences in resting-state functional brain connectivity in autistic males from childhood to mid-adulthood.

Researchers studied brain connections in over 1,000 males with and without autism from ages 5-40. They found that brain networks develop differently in autism. Some brain connections were consistently weaker in autism throughout development, while others started similarly but became different during the teenage years. This helps explain why previous studies found mixed results about brain connectivity in autism.

This study examined brain connectivity patterns in 1,107 males with and without autism (ages 5-40) using advanced statistical modeling. Researchers found that brain networks in autism show different developmental trajectories compared to typical development. While some connections (like default mode to central executive networks) developed similarly between groups, others showed persistent differences. Notably, autism was associated with chronic underconnectivity in default mode-salience and default mode-somatomotor networks throughout development.

Within-network somatomotor connectivity appeared similar in childhood but diverged during adolescence, with autism showing decreased connectivity. The flexible modeling approach revealed complex, nonlinear developmental patterns that may explain inconsistencies in previous autism brain connectivity research.

Findings suggest adolescence may be a critical intervention period for motor skills. The persistent underconnectivity in attention and sensory networks may inform targeted interventions for attention regulation and sensory processing difficulties throughout development in autism.

Study limited to males only, restricted age range (5-40 years), cross-sectional design, and large-scale network analysis only. Fine-grained parcellation including subcortical areas may provide more specific insights. Early development and later life aging patterns remain unexplored.

Divergent age-related functional brain connectivity in autism spectrum disorder (ASD) has been observed using resting-state fMRI, although the specific findings are inconsistent across studies. Common statistical regression approaches that fit identical models across functional brain networks may contribute to these inconsistencies. Relationships among functional networks have been reported to follow unique nonlinear developmental trajectories, suggesting the need for flexible modeling. Here we apply generalized additive models (GAMs) to flexibly adapt to distinct network trajectories and simultaneously describe divergent age-related changes from childhood into mid-adulthood in ASD. 1107 males, aged 5-40, from the ABIDE I & II cross-sectional datasets were analyzed.

Functional connectivity was extracted using a network-based template. Connectivity values were harmonized using COMBAT-GAM. Connectivity-age relationships were assessed with thin-plate spline GAMs. Post-hoc analyses defined the age-ranges of divergent aging in ASD.

Typically developing (TD) and ASD groups shared 15 brain connections that significantly changed with age (FDR-corrected p < 0.05). Network connectivity exhibited diverse nonlinear age-related trajectories across the functional connectome. Comparing ASD and TD groups, default mode to central executive between-network connectivity followed similar nonlinear paths with no group differences. Contrarily, the ASD group had chronic hypoconnectivity throughout default mode-ventral attentional (salience) and default mode-somatomotor aging trajectories.

Within-network somatomotor connectivity was similar between groups in childhood but diverged in adolescence with the ASD group showing decreased within-network connectivity. Network connectivity between the somatomotor network and various other functional networks had fully disrupted age-related pathways in ASD compared to TD, displaying significantly different model curvatures and fits. The present analysis includes only male participants and has a restricted age range, limiting analysis of early development and later life aging, years 40 and beyond. Additionally, our analysis is limited to large-scale network cortical functional parcellation.

To parse more specificity of brain region connectivity, a fine-grained functional parcellation including subcortical areas may be warranted. Flexible non-linear modeling minimizes statistical assumptions and allows diagnosis-related brain connections to follow independent data-driven age-related pathways. Using GAMs, we describe complex age-related pathways throughout the human connectome and observe distinct periods of divergence in autism.